Speakers

Conference Keynote Speakers

Kevin Leyton-Brown, Tuesday, May 26^th, 11:30–12:30

The University of British Columbia (UBC), Vancouver

“Algorithm synthesis with theoretical guarantees”

Despite massive progress on LLM-driven code synthesis, synthesizing algorithms with high empirical performance requires extensive, extremely expensive empirical testing. Algorithm configuration methods are automated ways of performing such testing: optimizing the performance of parameterized heuristic algorithms on given distributions of problem instances. Such methods can be seen as efficient procedures for extending classical machine learning to hypothesis spaces consisting of algorithm designs. This talk will begin by defining the problem and illustrating its promise via some recent practical success stories. However, all widely used algorithm configuration methods both achieve poor asymptotic runtime performance in the worst case and optimize what I will argue is the wrong objective function. I will begin by explaining why we should leverage decision theory to maximize expected utility instead of minimizing average runtime. Then I will present a new algorithm configuration approach called Continuous, Online Utilitarian Procrastination (COUP), which optimizes this objective while offering strong theoretical guarantees. I will conclude by showing that these guarantees come effectively for free, as COUP achieves state-of-the-art empirical performance.

Kevin Leyton-Brown is a professor of Computer Science and a Distinguished University Scholar at the University of British Columbia. He holds a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute and is an associate member of the Vancouver School of Economics. He received a PhD and an M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies artificial intelligence, mostly at the intersection of machine learning with either the design and operation of electronic markets or the design of heuristic algorithms. He has helped to design a government auction that reallocated North American radio spectrum; an electronic market that linked Ugandan farmers with buyers for surplus crops; and widely used open source software such as SATzilla (an algorithm portfolio for solving satisfiability problems), Mechanical TA (peer grading software used at universities around the world), and AutoWEKA (a machine learning tool that both selects a model family and optimizes its hyperparameters). He is increasingly interested in large language models, particularly as components of agent architectures. He believes we have both a moral obligation and a historical opportunity to leverage AI to benefit underserved communities, particularly in the developing world.

Sheila McIlraith, Wednesday, May 27^th, 11:30–12:30

University of Toronto

"Language as a signal-symbol nexus for human-compatible sequential decision making"

Striking advances in machine learning are transforming how we build sequential decision-making systems—from conversational agents and logistics planners to robots, and more generally to computer programs that automate a myriad of everyday tasks. The synthesis of decision-making systems from data presents fundamental challenges to how we effectively integrate and leverage human know-how, capture human norms and preferences, adhere to safety and regulatory constraints, and how we build systems that are taskable by and understandable to the humans they interact with. In this talk, I’ll discuss how language—both formal and natural language—provides a signal-symbol nexus for building human-compatible AI sequential decision-making systems. Not only does language provide an expressive and concise vehicle for communication, but I’ll show that exploiting the compositional syntax and semantics of language can greatly improve the efficiency of learning.

Sheila McIlraith is a Professor in the Department of Computer Science, University of Toronto, a CIFAR AI Chair (Vector Institute), and an Associate Director and Research Lead of the Schwartz Reisman Institute for Technology and Society. McIlraith's research is in the areas of AI knowledge representation, automated reasoning and machine learning. Her research focuses on AI sequential decision making, broadly construed, through the lens of human-compatible AI. McIlraith is a fellow of the ACM, a fellow of AAAI, and a Schmidt Sciences AI2050 Senior Fellow. She is a member of the Canadian AI Safety Institute (CAISI) Research Council and is currently serving as Chair of the Standing Committee of the One Hundred Year Study on Artificial Intelligence (AI100). McIlraith has served on the editorial boards of AIJ, JAIR, and Artificial Intelligence Magazine. She has also served as program co-chair of AAAI2018, KR2012, and ISWC2004, as well as conference co-chair of ICAPS2024. McIlraith and co-authors have been honoured with a number of paper recognitions, including three test-of-time style awards. She has also been honoured with a CAIAC Lifetime Achievement Award for research excellence in AI. McIlraith initiated and co-leads the University of Toronto Embedded Ethics Education Initiative (E3I) with annual student enrollment in E3I programming nearing 10,000.

Speakers for our Platinum Sponsors

Katie Seguin, Tuesday, May 26 2026, 11:00-11:30

Mental Health Commission of Canada

“Artificial Intelligence Guidance in Mental Health and Substance Use Health Care in Canada”

Kelsey Allen, Tuesday evening, May 26^th, Reception, 19:00-21:00

CIFAR & The University of British Columbia (UBC), Vancouver

“Minds and Machines: The Evolving Loop Between Cognitive Science and AI”

Kelsey is an assistant professor of Computer Science and Psychology at UBC, a CIFAR Azrieli Global Scholar in the Learning in Machines and Brains program, and a CIFAR AI Chair at Vector. Previously, she was a Senior Research Scientist at Google DeepMind. She received her PhD from MIT in Brain and Cognitive Sciences. Her work has received awards including the international Glushko prize for best dissertation in cognitive science and best paper awards from Robotics: Science and Systems (R:SS) and multiple machine learning conference workshops. Spanning robotics, machine learning, and cognitive science, her work aims to elucidate the mechanisms that give rise to adaptive and efficient learning, especially in the domain of physical problem-solving.

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